基于改进形态-小波阈值降噪的轴承复合故障声学诊断

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (12) : 221-226.

论文

樊高瞻，周俊，朱昆莉

作者信息 +

An improved morphological-wavelet threshold de-noising method based acoustic diagnosis for bearing composite faults

FAN Gaozhan,ZHOU Jun,ZHU Kunli

Author information +

文章历史 +

摘要

现场采集的滚动轴承复合故障声学信号存在噪声来源复杂、背景噪声强、非线性等特点，导致已知的自适应多尺度形态滤波不能很好的适用于轴承复合故障的盲分离。针对上述问题，提出一种基于改进的自适应多尺度多结构形态滤波(IAMSCMF)、改进的小波阈值降噪方法(IWTDM)和稀疏量分析(SCA)相结合的滚动轴承复合故障特征盲提取方法。首先利用IAMSCMF和IWTDM构造滤波器进行滤波及提高信噪比(SNR)；其次利用SCA分离信号；最后用FFT进行频谱分析。仿真分析和滚动轴承现场采集声学信号分析结果均清晰的提取出了轴承故障特征，验证了算法的有效性。

Abstract

Sound signals of rotating bearing compound faults acquired in the actual field have characteristics of complex noise sources, the strong background noise and the nonlinearity, causing traditional adaptive multi scale morphology filtering are not completely suitable for blind extraction of composite bearing faults. According to these problems, a method based on Improved Adaptive Multi Scale Compound Morphology Filter (IAMSCMF), Improved Wavelet Threshold De-noising Method (IWTDM) and Sparse Component Analysis (SCA) was presented to identify bearing faults. First, IAMSCMF and IWTDM were used to reduce noise and to improve Signal to Noise Rate (SNR), and then, using SCA to separate signals, at last, FFT calculation was used to deal with the spectrum analysis. The results of simulations and real rolling bearing sound signals analysis show that the method can extract the bearing fault characteristics, verifying the effectiveness of the proposed algorithm.

导出引用

樊高瞻，周俊，朱昆莉. 基于改进形态-小波阈值降噪的轴承复合故障声学诊断[J]. 振动与冲击, 2020, 39(12): 221-226

FAN Gaozhan,ZHOU Jun,ZHU Kunli . An improved morphological-wavelet threshold de-noising method based acoustic diagnosis for bearing composite faults[J]. Journal of Vibration and Shock, 2020, 39(12): 221-226

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